ROI Calculator
See the Value of Smarter Experimentation
GrowthBook helps teams ship better experiences faster, cut engineering hours, and drive measurable revenue impact. Use this transparent calculator to estimate your return in under a minute.
How Does GrowthBook Create Value?
Product development teams using GrowthBook create value by improving productivity, shipping better products, and reducing tech costs. Here’s how.
Faster Experimentation & Less Engineering Burden
GrowthBook cuts hours of dev time, setup, and analysis work so teams can test more ideas without burning cycles.
Example: If your team runs 200 experiments per month and saves an average of 10–15 hours each, that’s thousands of hours freed up every year for higher-value work.
Example: If your team runs 200 experiments per month and saves an average of 10–15 hours each, that’s thousands of hours freed up every year for higher-value work.
More Revenue per Visitor or User
Better experimentation improves both how many people convert and how much they’re worth when they do.
Example: A small lift in conversion rate or a bump in average order value can translate into hundreds of additional purchases and a meaningful revenue increase.
Example: A small lift in conversion rate or a bump in average order value can translate into hundreds of additional purchases and a meaningful revenue increase.
Faster Rollbacks of Problem Features
Feature flags give teams a safe way to release code and avoid incidents impacting customers.
Example: If a single incident costs your team $10–30k in engineering time and customer impact, preventing two or three a year pays for the platform on its own.
Example: If a single incident costs your team $10–30k in engineering time and customer impact, preventing two or three a year pays for the platform on its own.
Reduced Churn from Better Product Decisions
Retention is sensitive to product quality. Teams use GrowthBook to improve onboarding, engagement, and reactivation.
Example: A 0.3% improvement in gross renewal on a $10M ARR product equals $30,000 in protected revenue per month.
Example: A 0.3% improvement in gross renewal on a $10M ARR product equals $30,000 in protected revenue per month.
Retiring Home Grown Experimentation Tools
Many teams start with internal feature flags and ad-hoc analytics integrations that get expensive to maintain.
Example: 1-2 senior developers spending a few hours each week keeping internal tooling alive costs high-value weeks every year.
Example: 1-2 senior developers spending a few hours each week keeping internal tooling alive costs high-value weeks every year.
Consolidation of Expensive Tools
GrowthBook often replaces multiple point solutions: feature flags, A/B testing, targeting, rollouts, and more.
Example: Many teams reduce software spend by retiring an existing flagging tool, a separate experimentation platform, or both.
Example: Many teams reduce software spend by retiring an existing flagging tool, a separate experimentation platform, or both.
Calculate the Value of Smarter Experimentation
Answer a few questions about your business, and we’ll estimate your annual value instantly using assumptions based on average performance that we’ve seen.
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How This Calculator Works
Experimentation Hours Savings
Hours saved per experiment × # experiments per year × hourly cost
Revenue Uplift
(Incremental conversion % + Average order value increase % ) × opportunity volume × AOV
Churn Reduction
Improvement in renewal % × total ARR
Incident Hours Savings
Average downtime cost per hour × reduction in incident hours
Software Development Savings
Dev hours spent maintaining and enhancing custom feature flag and experimentation systems × hourly developer cost
Tool Consolidation Savings
Current annual tool spend replaced by GrowthBook
Teams Like Yours See Results with GrowthBook
Upstart
Faster Experimentation
Launch in hours, not days
Tool Consolidation
From 3 platforms to 1
Resource Optimization
Significant cost savings
Read the Full Story >
Breeze Airways
Doubled testing throughput
without adding headcount
Unlocked $1MM+
in incremental monthly revenue from experiments
Avoided $MM mistakes
through “do no harm” testing
Read the Full Story >